UMAP projections show sample groups (left), cell type annotations (middle), and CHIKV+ cells (right).



The fraction of cells passing QC filtering cutoffs is shown for each sample.



Macrophages


Subset annotations

UMAP projections show macrophage subsets.



Module scores for Culemann et al. gene lists are shown for each macrophage cluster. Clusters are colored based on the assigned subset labels.



Heatmaps show mean expression of Culemann et al. gene lists. Replicates are shown separately for each macrophage subset.

CCR2_ARG1_infiltrating



CCR2_IL1B_infiltrating



CX3CR1_lining



RELMa_interstitial



AQP1_interstitial



CHIKV+ cells

UMAP projections show CHIKV+ cells (left) and CHIKV-high clusters (middle, right) for CHIKV-infected samples.



The fraction of cells that are CHIKV+ is shown for each replicate from each macrophage subset. P values were calculated by pooling replicates together, using a hypergeometric test with Bonferroni correction.



The fraction of CHIKV+ cells passing QC filtering cutoffs is shown for each sample.



Differential expression

Top 50 upregulated genes associated with arthritis are shown below for mock- vs CHIKV-infected macrophages.



Genes associated with antigen presentation via MHC class II are shown below. Boxplots compare expression in mock (light) vs CHIKV (dark) for each macrophage subset.



Top 60 upregulated genes in CHIKV-infected mice are shown below for each macrophage subset.

Upset plots (top) show the overlap between differentially expressed genes identified for each subset. Subsets with overlapping genes are connected by a black line, bars show the number of genes. A single point (not connected by a black line) shows the number of unique genes for the subset.

AQP1_interstitial



CX3CR1_lining



infiltrating



RELMa_interstitial



unassigned



Simmons et al. 

Expression of Simmons et al. gene lists is shown below.

SLAMF7_stim



SLAMF7_high



arthritis



T cells


Subset annotations

UMAP projections show T cell subsets.



Key markers are shown below for T cells from mock- and CHIKV-infected samples.



Ifng expression is shown below for each T cell subset.



Differential expression

Top 80 genes upregulated in CHIKV vs mock-infected mice are shown below, all T cells were grouped together.



Genes upregulated in T cells from CHIKV vs mock-infected mice are shown below. All CD4+ or CD8+ T cells were grouped together.



Session info

## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C                 
##  [3] LC_TIME=en_US.UTF-8           LC_COLLATE=en_US.UTF-8       
##  [5] LC_MONETARY=en_US.UTF-8       LC_MESSAGES=en_US.UTF-8      
##  [7] LC_PAPER=en_US.UTF-8          LC_NAME=en_US.UTF-8          
##  [9] LC_ADDRESS=en_US.UTF-8        LC_TELEPHONE=en_US.UTF-8     
## [11] LC_MEASUREMENT=en_US.UTF-8    LC_IDENTIFICATION=en_US.UTF-8
## 
## time zone: America/Denver
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] qs_0.25.7                   edgeR_4.0.12               
##  [3] limma_3.58.1                metap_1.9                  
##  [5] harmony_1.2.0               gprofiler2_0.2.2           
##  [7] presto_1.0.0                data.table_1.14.10         
##  [9] Rcpp_1.0.11                 M3Drop_1.28.0              
## [11] numDeriv_2016.8-1.1         DoubletFinder_2.0.3        
## [13] scuttle_1.12.0              SingleCellExperiment_1.24.0
## [15] SummarizedExperiment_1.32.0 GenomicRanges_1.54.1       
## [17] GenomeInfoDb_1.38.5         MatrixGenerics_1.14.0      
## [19] matrixStats_1.2.0           clustifyrdata_1.1.0        
## [21] clustifyr_1.14.0            SeuratObject_4.1.4         
## [23] Seurat_4.4.0                ggupset_0.3.0              
## [25] patchwork_1.1.3             ggrepel_0.9.4              
## [27] scales_1.3.0                djvdj_0.1.0                
## [29] colorblindr_0.1.0           colorspace_2.1-0           
## [31] ggtrace_0.2.0.9000          furrr_0.3.1                
## [33] future_1.33.1               xlsx_0.6.5                 
## [35] knitr_1.45                  cowplot_1.1.2              
## [37] here_1.0.1                  broom_1.0.5                
## [39] lubridate_1.9.3             forcats_1.0.0              
## [41] stringr_1.5.1               dplyr_1.1.4                
## [43] purrr_1.0.2                 readr_2.1.4                
## [45] tidyr_1.3.0                 tibble_3.2.1               
## [47] ggplot2_3.4.4               tidyverse_2.0.0            
## [49] org.Hs.eg.db_3.18.0         org.Mm.eg.db_3.18.0        
## [51] AnnotationDbi_1.64.1        IRanges_2.36.0             
## [53] S4Vectors_0.40.2            Biobase_2.62.0             
## [55] BiocGenerics_0.48.1         biomaRt_2.58.0             
## [57] DOSE_3.28.2                 msigdbr_7.5.1              
## [59] enrichplot_1.22.0           clusterProfiler_4.10.0     
## 
## loaded via a namespace (and not attached):
##   [1] vroom_1.6.5               progress_1.2.3           
##   [3] nnet_7.3-19               goftest_1.2-3            
##   [5] Biostrings_2.70.1         TH.data_1.1-2            
##   [7] rstan_2.32.3              vctrs_0.6.5              
##   [9] spatstat.random_3.2-2     RApiSerialize_0.1.2      
##  [11] digest_0.6.33             png_0.1-8                
##  [13] deldir_2.0-2              parallelly_1.36.0        
##  [15] MASS_7.3-60               reshape2_1.4.4           
##  [17] httpuv_1.6.13             qvalue_2.34.0            
##  [19] withr_2.5.2               xfun_0.41                
##  [21] ggfun_0.1.3               ellipsis_0.3.2           
##  [23] survival_3.5-7            memoise_2.0.1            
##  [25] gson_0.1.0                tidytree_0.4.6           
##  [27] zoo_1.8-12                gtools_3.9.5             
##  [29] pbapply_1.7-2             entropy_1.3.1            
##  [31] Formula_1.2-5             prettyunits_1.2.0        
##  [33] KEGGREST_1.42.0           promises_1.2.1           
##  [35] httr_1.4.7                globals_0.16.2           
##  [37] fitdistrplus_1.1-11       stringfish_0.16.0        
##  [39] rstudioapi_0.15.0         miniUI_0.1.1.1           
##  [41] generics_0.1.3            base64enc_0.1-3          
##  [43] babelgene_22.9            curl_5.2.0               
##  [45] zlibbioc_1.48.0           ggraph_2.1.0             
##  [47] TFisher_0.2.0             polyclip_1.10-6          
##  [49] GenomeInfoDbData_1.2.11   SparseArray_1.2.3        
##  [51] xtable_1.8-4              evaluate_0.23            
##  [53] S4Arrays_1.2.0            BiocFileCache_2.10.1     
##  [55] hms_1.1.3                 irlba_2.3.5.1            
##  [57] filelock_1.0.3            ROCR_1.0-11              
##  [59] reticulate_1.34.0         spatstat.data_3.0-3      
##  [61] magrittr_2.0.3            lmtest_0.9-40            
##  [63] later_1.3.2               viridis_0.6.4            
##  [65] ggtree_3.10.0             lattice_0.22-5           
##  [67] spatstat.geom_3.2-7       future.apply_1.11.1      
##  [69] scattermore_1.2           XML_3.99-0.16            
##  [71] shadowtext_0.1.2          RcppAnnoy_0.0.21         
##  [73] StanHeaders_2.26.28       Hmisc_5.1-1              
##  [75] pillar_1.9.0              nlme_3.1-164             
##  [77] caTools_1.18.2            compiler_4.3.1           
##  [79] beachmat_2.18.0           stringi_1.8.3            
##  [81] tensor_1.5                plyr_1.8.9               
##  [83] crayon_1.5.2              abind_1.4-5              
##  [85] gridGraphics_0.5-1        sn_2.1.1                 
##  [87] locfit_1.5-9.8            sp_2.1-2                 
##  [89] mathjaxr_1.6-0            graphlayouts_1.0.2       
##  [91] bit_4.0.5                 sandwich_3.1-0           
##  [93] fastmatch_1.1-4           multcomp_1.4-25          
##  [95] codetools_0.2-19          QuickJSR_1.0.9           
##  [97] bslib_0.6.1               plotly_4.10.3            
##  [99] multtest_2.58.0           mime_0.12                
## [101] splines_4.3.1             dbplyr_2.4.0             
## [103] sparseMatrixStats_1.14.0  HDO.db_0.99.1            
## [105] blob_1.2.4                utf8_1.2.4               
## [107] reldist_1.7-2             fs_1.6.3                 
## [109] listenv_0.9.0             checkmate_2.3.1          
## [111] DelayedMatrixStats_1.24.0 Rdpack_2.6               
## [113] pkgbuild_1.4.3            ggplotify_0.1.2          
## [115] Matrix_1.6-1.1            statmod_1.5.0            
## [117] tzdb_0.4.0                tweenr_2.0.2             
## [119] pkgconfig_2.0.3           tools_4.3.1              
## [121] cachem_1.0.8              rbibutils_2.2.16         
## [123] RSQLite_2.3.4             viridisLite_0.4.2        
## [125] DBI_1.2.0                 fastmap_1.1.1            
## [127] rmarkdown_2.25            grid_4.3.1               
## [129] ica_1.0-3                 sass_0.4.8               
## [131] RANN_2.6.1                rpart_4.1.23             
## [133] farver_2.1.1              mgcv_1.9-1               
## [135] tidygraph_1.3.0           scatterpie_0.2.1         
## [137] yaml_2.3.8                foreign_0.8-86           
## [139] cli_3.6.2                 leiden_0.4.3.1           
## [141] lifecycle_1.0.4           uwot_0.1.16              
## [143] mvtnorm_1.2-4             backports_1.4.1          
## [145] BiocParallel_1.36.0       timechange_0.2.0         
## [147] gtable_0.3.4              ggridges_0.5.5           
## [149] densEstBayes_1.0-2.2      progressr_0.14.0         
## [151] parallel_4.3.1            ape_5.7-1                
## [153] jsonlite_1.8.8            bitops_1.0-7             
## [155] bit64_4.0.5               qqconf_1.3.2             
## [157] Rtsne_0.17                yulab.utils_0.1.2        
## [159] spatstat.utils_3.0-4      RcppParallel_5.1.7       
## [161] mutoss_0.1-13             bdsmatrix_1.3-6          
## [163] highr_0.10                jquerylib_0.1.4          
## [165] loo_2.6.0                 GOSemSim_2.28.0          
## [167] lazyeval_0.2.2            shiny_1.8.0              
## [169] htmltools_0.5.7           rJava_1.0-10             
## [171] GO.db_3.18.0              sctransform_0.4.1        
## [173] rappdirs_0.3.3            glue_1.6.2               
## [175] XVector_0.42.0            RCurl_1.98-1.13          
## [177] rprojroot_2.0.4           treeio_1.26.0            
## [179] mnormt_2.1.1              gridExtra_2.3            
## [181] igraph_1.6.0              R6_2.5.1                 
## [183] gplots_3.1.3              labeling_0.4.3           
## [185] xlsxjars_0.6.1            cluster_2.1.6            
## [187] bbmle_1.0.25.1            aplot_0.2.2              
## [189] plotrix_3.8-4             rstantools_2.3.1.1       
## [191] DelayedArray_0.28.0       tidyselect_1.2.0         
## [193] htmlTable_2.4.2           inline_0.3.19            
## [195] ggforce_0.4.1             xml2_1.3.6               
## [197] munsell_0.5.0             KernSmooth_2.23-22       
## [199] htmlwidgets_1.6.4         fgsea_1.28.0             
## [201] RColorBrewer_1.1-3        rlang_1.1.2              
## [203] spatstat.sparse_3.0-3     spatstat.explore_3.2-5   
## [205] fansi_1.0.6